Expectations

• One workshop per week

• One lecture per week

• One assignment per week

• One ‘capstone’ project

What to expect during this course

What is Reproducibility?

Introduction to Reproducible Research

• For Research to be reproducible both data and methods should be available.

• Applying the described methods to the data leads to the same results

Methods

• In theory, method availability ≠ code

• But with complex data and analyses - are methods of data collection enough?

Self-correcting Science?

• Science advances incrementally by identifying and rectifying errors over time

• Peer review: Critical evaluation of papers by experts maintain quality

• Independent studies either support or fail to replicate findings

Self-correcting Science?

• Publication bias: preference for positive results

• Pressure to publish

• Poor study designs and statistical issues

• Lack of transparency

Reproducibility trial:

246 biologists get different results from same data sets

Reproducibility Crisis

• The reproducibility crisis emerged when numerous studies, especially in fields like psychology, medicine, and biology, failed to be replicated by other researchers.

• High-profile replication attempts revealed that many published results could not be consistently reproduced, raising doubts about their validity.

Crisis as an Opportunity

• Recognition that no study should be considered ‘definitive’

• Empower lasting systemic change through increased transparency in research methods, data sharing and reporting

• Structural change in academic culture

Open Science

Open science is a global movement that aims to make scientific research and its outcomes freely accessible to everyone. By fostering practices like data sharing and preregistration, open science not only accelerates scientific progress but also strengthens trust in research findings.

UKRN

Tidy projects

/home/phil/Documents/paper
├── abstract.R
├── correlation.png
├── data.csv
├── data2.csv
├── fig1.png
├── figure 2 (copy).png
├── figure.png
├── figure1.png
├── figure10.png
├── partial data.csv
├── script.R
└── script_final.R

Organised projects

• README

• Documented

• Easy to code with

• All files are inside the root folder

R projects

Slugs

• A string of characters defining a file

What do you think are the contents of these files:

• data/raw/madrid_minimum-temperature.csv

• scripts/02_compute_mean-temperature.R

• analysis/01_madrid_minimum-temperature_descriptive-statistics.qmd

Name files

Come up with good names for these:

• a dataset of cats with columns for weight, length, tail length, fur colour(s), fur type and name.

• a script that downloads data from Spotify.

• a scripts that cleans up data.

• a scripts that fits a linear discriminant model and saves it to a file.

R projects and clean slates

• Use projects

• Check your code runs on blank slates

Quarto

Git

Git repository

Git collab

Forking

Renv

Benefits

Additional resources

• Discovering Statistics - Andy Field

• Happy Git

• An Introduction to Generalized Linear Models - Dobson & Barnett

• An Introduction to Statistical Learning with Applications in R - James, Witten, Hastie & Tibshirani

• Mixed Effects Models and Extensions in Ecology with R - Zuur, et al.

• Ecological Statistics with contemporary theory and application

• The Big Book of R (https://www.bigbookofr.com/)

• British Ecological Society Guides to Better Science

*(SORTEE)